Bias Reduction for Dynamic Nonlinear Panel Models With Fixed Effects

This paper considers model selection of nonlinear panel data models in the presence of incidental parameters (i.e., large-dimensional nuisance parameters). The main interest is in selecting the model that approximates best the structure with the common parameters after concentrating out the incidental parameters. New model selection information criteria are developed that use either the Kullback-Leibler information criterion based on the profile likelihood or the Bayes factor based on the integrated likelihood with the robust prior of Arellano and Bonhomme (2009, Econometrica 77: 489-536). These model selection criteria impose heavier penalties than those of the standard information criteria such as AIC and BIC. The additional penalty, which is data-dependent, properly reflects the model complexity from the incidental parameters. As a particular example, a lag order selection criterion is examined in the context of dynamic panel models with fixed individual effects, and it is illustrated how the over/under-selection probabilities are controlled for.

show abstract

“…The remainder term is negligible as shown in Hahn and Kuersteiner (2011) for   → ∞ satisfying  →  ∈ (0 ∞) and  3 → 0.…”

Section: Proof Of Theorem 2 For Eachmentioning

confidence: 96%

“…Since b   is √  -consistent to  0 under the conditions in Theorem 2 (e.g., the Lyapounov's theorem; Sartori (2003), Hahn and Kuersteiner (2011)) 14 and by the Envelope theorem, a consistent estimator for   () under   → ∞ can be obtained as (28) using the fact that…”

Section: Proof Of Theorem 2 For Eachmentioning

confidence: 99%

Model selection in the presence of incidental parameters

Lee

Phillips

2015

Journal of Econometrics

View full text Add to dashboard Cite

show abstract

“…Following Hahn and Newey (2004) and Hahn and Kuersteiner (2011), the profile log-likelihood function is…”

Section: Penalized Profile Likelihood Estimation Of α and βmentioning

confidence: 99%

Identifying Latent Structures in Panel Data

Shi

Phillips

2014

SSRN Journal

View full text Add to dashboard Cite

This paper provides a novel mechanism for identifying and estimating latent group structures in panel data using penalized techniques. We consider both linear and nonlinear models where the regression coefficients are heterogeneous across groups but homogeneous within a group and the group membership is unknown. Two approaches are considered -penalized profile likelihood (PPL) estimation for the general nonlinear models without endogenous regressors, and penalized GMM (PGMM) estimation for linear models with endogeneity. In both cases we develop a new variant of Lasso called classifier-Lasso (C-Lasso) that serves to shrink individual coefficients to the unknown group-specific coefficients. CLasso achieves simultaneous classification and consistent estimation in a single step and the classification exhibits the desirable property of uniform consistency. For PPL estimation C-Lasso also achieves the oracle property so that group-specific parameter estimators are asymptotically equivalent to infeasible estimators that use individual group identity information. For PGMM estimation the oracle property of C-Lasso is preserved in some special cases. Simulations demonstrate good finite-sample performance of the approach both in classification and estimation. Empirical applications to both linear and nonlinear models are presented.JEL Classification: C33, C36, C38, C51

show abstract

“…The linear CRC panel data models can be motivated as follows, which is given in Hahn (2001). Suppose we have an unobserved fixed effects panel probit model with two periods,…”

Section: Identification Of Linear Crc Modelsmentioning

confidence: 99%

“…In the analysis of treatment effect, under certain circumstances, the binary choice fixed-effects model can be transferred to a linear random coefficient model with the average treatment effect being the mean of a random coefficient. For instance, in one of the commenting papers for Angrist (2001), Hahn (2001) gives an example on this transformation and discusses the consistency of the fixed effects estimator. Wooldridge (2005) further allows the correlation between regressors and random coefficients and gives the conditions that assure the consistency of the fixed effects estimator.…”

Section: Introductionmentioning

confidence: 99%